Towards Cover Song Detection with Siamese Convolutional Neural Networks

Autor:	Stamenovic, Marko
Rok vydání:	2020
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Machine Learning Computer Science - Sound Statistics - Machine Learning
Zdroj:	Proceedings of the 35th International Conference on Machine Learning, Stockholm, Sweden, PMLR 80, 2018
Druh dokumentu:	Working Paper
Popis:	A cover song, by definition, is a new performance or recording of a previously recorded, commercially released song. It may be by the original artist themselves or a different artist altogether and can vary from the original in unpredictable ways including key, arrangement, instrumentation, timbre and more. In this work we propose a novel approach to learning audio representations for the task of cover song detection. We train a neural architecture on tens of thousands of cover-song audio clips and test it on a held out set. We obtain a mean precision@1 of 65% over mini-batches, ten times better than random guessing. Our results indicate that Siamese network configurations show promise for approaching the cover song identification problem. Comment: Code available at https://github.com/markostam/coversongs-dual-convnet
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2005.10294 Zobrazit plný text záznamu View this record from Arxiv